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Open Access Highly Accessed Research article

Protein subcellular localization prediction based on compartment-specific features and structure conservation

Emily Chia-Yu Su12, Hua-Sheng Chiu3, Allan Lo14, Jenn-Kang Hwang2, Ting-Yi Sung3 and Wen-Lian Hsu3*

Author Affiliations

1 Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan

2 Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan

3 Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan

4 Department of Life Sciences, National Tsing Hua University, Hsinchu, Taiwan

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BMC Bioinformatics 2007, 8:330  doi:10.1186/1471-2105-8-330

Published: 8 September 2007



Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.


We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.


Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.